def GIS():

    global C

    global FeaWeights

    for wid in WordDic.keys():

        FeaWeights[wid] = {}

        for classid in ClassList:

            FeaWeights[wid][classid] = 0.0

    n = 0

    prelogllh = -1000000.0

    logllh = -10000.0

    while logllh - prelogllh >= LogLLDiff and n < MaxIteration:

        n += 1

        prelogllh = logllh

        logllh = 0.0

        print "Iteration", n

        for wid in WordDic.keys():

            for classid in ClassList:

                FeaClassTable[wid][1][classid] = 0.0

        #compute expected values of features subject to the model p(y|x)

        for doc in DocList:

            classProbs = [0.0] * len(ClassList)

            sum = 0.0

            for i in range(len(ClassList)):

                classid = ClassList[i]

                pyx = 0.0

                for wid in doc[0].keys():

                    pyx += FeaWeights[wid][classid]

                pyx = math.exp(pyx)

                classProbs[i] = pyx

                sum += pyx

            for i in range(len(ClassList)):

                classProbs[i] = classProbs[i] / sum

            for i in range(len(ClassList)):

                classid = ClassList[i]

                if classid == doc[1]:

                    logllh += math.log(classProbs[i])

                for wid in doc[0].keys():

                    FeaClassTable[wid][1][classid] += classProbs[i] * doc[0][wid]

        #update feature weights

        for wid in WordDic.keys():

            for classid in ClassList:

                empValue = 0.0

                if classid in FeaClassTable[wid][0]:

                    empValue = FeaClassTable[wid][0][classid]

                modelValue = 0.0

                if classid in FeaClassTable[wid][1]:

                    modelValue = FeaClassTable[wid][1][classid]

                if empValue == 0.0 or modelValue == 0.0:

                    continue

                FeaWeights[wid][classid] += math.log(

                    FeaClassTable[wid][0][classid] / FeaClassTable[wid][1][classid]) / C

        print "Loglikelihood:", logllh

    return